###### "Markov Chain Monte Carlo With Application to Image Denoising" by Jakub Michel
This algorithm can be used in such a way that it takes into account the prior distribution and likelihood function, carrying a ... This algorithm can be used in such a way that it takes into account the prior distribution and likelihood function, carrying a ...
https://bearworks.missouristate.edu/theses/1649/
###### "Deaf students and their classroom communication: an evaluation of high" by Thomas E. Allen and Melissa L. Anderson
... reducing the likelihood of speech only and increasing the placement of students into signing classroom settings. Implications ... Likelihood Functions; Parents; Persons With Hearing Impairments; *Schools; Sign Language; Socialization; Speech; Teaching ... reducing the likelihood of speech only and increasing the placement of students into signing classroom settings. Implications ...
https://escholarship.umassmed.edu/psych_cmhsr/653/
###### R: Method for Profiling nls Objects
Investigates the profile log-likelihood function for a fitted model of class "nls". . ...
http://stat.ethz.ch/R-manual/R-devel/library/stats/html/profile.nls.html
###### Identification of reference miRNAs in plasma useful for the study of oestrogen-responsive miRNAs associated with acquired...
Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika. 1974;61(3):439-47.Google ... MiRNAs are short, non-coding RNA species approximately 22 nucleotides in length that function to downregulate the expression of ... they do have ascribed biological functions. In hepatocellular carcinoma cells, miR-188-5p was shown to inhibit growth of ... Digital Analyser were compared overall among the four cohorts and via pairwise cohort comparisons based on quasi-likelihood ...
https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-2636-3
###### Selection of ordinally scaled independent variables with applications to international classification of functioning core sets ...
... a generalized Bradley-Terry model for continuous data using a discontinuous penalized likelihood function Next article in issue ... a generalized Bradley-Terry model for continuous data using a discontinuous penalized likelihood function ... We consider so-called international classfication of functioning core sets for chronic widespread pain, in which many ordinal ... The effect of specific international classification of functioning variables on a subjective measure of physical health is ...
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9876.2010.00753.x/abstract
###### US7092436B2 - Expectation-maximization-based channel estimation and signal detection for wireless communications systems ...
... estimate of the symbol is computed by using updated channel information and maximizing the expectation of the log likelihood ... function. The next estimate is then quantized according to the signal constellation. The quantized estimate of the symbol is ... where the log likelihood function can be expressed as log p( Y, h,X )=log p( Y,h, X )+log p( h,X ). (11) ... symbol which maximizes an expectation of a log likelihood function by averaging a logarithm of a likelihood function over ...
###### Math Forum Discussions
likelihood function:. The model is of typical loglikelihood function:. f(mu, sigma, a, b)=-0.5*sum_of_squares( (x - mu)/sigma ... Thus this 4D log likelihood function is hard to maximize using data. I am. trying many solvers.. As you can see, theoretically ... because with z=f(x, y, u, v) 4D function, I was unable to systematically. visualize the sample points at all.. Any thoughts?. ... search for the global minimum of my 4D function z=f(x, y, u, v).. I have read literature extensively, and found out that there ...
###### Publications: Pathogen Evolution
Application of the likelihood function in phylogenetic analysis.. Huelsenbeck JP & Bollback JP (2001) In "Handbook of ...
http://tree.bio.ed.ac.uk/publications/pathogens/
###### Plus it
We computed the likelihood in this way and found the parameter set w that maximizes the likelihood function. ... likelihood of the parameter w given the observed data y; , probability density function of observation y given w; , probability ... The likelihood of the parameter set w is defined as the probability density function of the observation y given w. Since the ... We constructed the likelihood function based on the foregoing assumptions and found the parameter values that maximize the ...
http://www.plantphysiol.org/content/176/4/3046
###### Likelihood of Improving or Maintaining Renal Function in Recipients of Extended-Criteria Donor Kidneys over Five Years with...
Likelihood of Improving or Maintaining Renal Function over Five Years with Belatacept or CsA: Insights from the BENEFIT Long- ... Likelihood of Improving or Maintaining Renal Function in Recipients of Extended-Criteria Donor Kidneys over Five Years with ... atcmeetingabstracts.com/abstract/likelihood-of-improving-or-maintaining-renal-function-in-recipients-of-extended-criteria-donor ... Likelihood of Improving or Maintaining Renal Function in Recipients of Extended-Criteria Donor Kidneys over Five Years with ...
http://atcmeetingabstracts.com/abstract/likelihood-of-improving-or-maintaining-renal-function-in-recipients-of-extended-criteria-donor-kidneys-over-five-years-with-belatacept-or-csa-benefit-ext-long-term-extension-study/
###### Regresión loglineal (regresión de Poisson) | Software estadístico Excel
2 Log(Like.): The logarithm of the likelihood function associated with the model; ... This coefficient is equal to 1 minus the ratio of the likelihood of the adjusted model to the likelihood of the independent ... This coefficient is equal to 1 minus the ratio of the likelihood of the adjusted model to the likelihood of the independent ... This coefficient is equal to ratio of the R² of Cox and Snell, divided by 1 minus the likelihood of the independent model ...
https://www.xlstat.com/es/soluciones/funciones/regresion-loglineal-regresion-de-poisson
###### Application # 2017/0286365. APPROACH FOR MORE EFFICIENT USE OF COMPUTING RESOURCES WHILE CALCULATING CROSS PRODUCT OR ITS...
The underlying optimization problem is to minimize the negative log-likelihood function: l ( w ) = i = 1 m log ( 1 + exp ( - y ... As a result, when presented with a new set of predictors 105, the modeling computer 103 can use that function to predict the ... and .sigma.(p)=1/(1+exp(-p)) is the logistic sigmoid function. [0045] In an embodiment that uses parallel (or parallel ... For example, assume that the problem being solved by the modeling computer 103 is to determine a function that, based on ...
http://patents.com/us-20170286365.html
###### HyPhy message board - [ADDITION] Multiple Enhancements
A feature to automatically parallelize single likelihood function calculations accross an MPI cluster (AUTO_PARALLELIZE_MPI = 1 ... The abilitiy to serialize likelihood functions which depend on multiple data alignments. ...
http://www.hyphy.org/cgi-bin/hyphy_forums/YaBB.pl?num=1156262186/0
###### Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010-2014 | BMC Public Health | Full Text
Using the log-likelihood function $$\log L={\sum}_{i=1}^n\left\{{o}_i \log \left({e}_i{r}_i\right)-{e}_i{r}_i\right\}$$, the ... The likelihood function for window Ω is proportional to.. \( L\left(\Omega \right)=\begin{array}{c}\hfill \sup \hfill \\ {}\ ... Whenever the window finds a new case, the likelihood function for elevated risk within the window in comparison with those ... The window Ω∗ that attains the maximum likelihood is defined as the most likely hot-spot (MLH). We carried out the test of ...
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-017-4541-z
###### FUBAR - HyPhy Wiki
Computing site-by-site likelihoods at 20X20 grid points Computing the likelihood function on grid points 400/400 00:00:05 ... Computing the likelihood function on grid points 400/400 00:00:05 Best scaling achieved for dN/dS = 1.00. ... Evaluate the likelihood of the Muse-Gaut 94 model on each (αβ) grid point. The point which maximizes the likelihood is then ... a self-contained NEXUS serialized likelihood function containing the MG94 model fit with the best branch length scaling ...
http://www.hyphy.org/w/index.php?title=FUBAR&oldid=2661
###### Entscheidungsanalyse - Eawag
Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The ... This covers uncertainty of the preference components (marginal value and utility functions, hierarchical aggregation functions ... the overall risk attitude and aggregation functions at higher-level nodes. Here, rough value function elicitation was ... To address (ii), we emphasize the importance of value functions, in addition to utilities, to support decisions under risk. We ...
https://www.eawag.ch/de/abteilung/ess/schwerpunkte/entscheidungsanalyse/
###### Population Pharmacokinetic-Pharmacodynamic Modeling of Epidural Anesthesia | Anesthesiology | ASA Publications
... likelihood ratio tests were performed (Δobjective function value , 3.84 corresponds to P , 0.05; Δobjective function value , ... where the population values μAthrand μke0are functions of age as given by equation 5, and the parameters of A e(t ; k e0) ( ... By maximizing this likelihood, the parameters of the lognormal distributions of A thrand k e0can be obtained. (Note that NONMEM ... The likelihood of an observation in individual i is given by the (double) integral of the probability distributions of A thrand ...
http://anesthesiology.pubs.asahq.org/article.aspx?articleid=1922229
###### Outbreaks of Streptococcus pneumoniaecarriage in day care cohorts in Finland - implications for elimination of transmission |...
... the likelihood function of the model parameters), P c is the likelihood function of the complete data (is one if the complete ... The likelihood function for n individuals on the time interval ]t min, t max] defined by model (1) is. ... The complete data likelihood function. For individual i, denote by the set of times the carriage status changes from state r to ... Making likelihood-based inference about the model parameter θ = {β fam , β dcc , κ, μ, φ, η, δ} requires knowing the exact ...
https://bmcinfectdis.biomedcentral.com.preview-live.oscarjournals.springer.com/articles/10.1186/1471-2334-9-102
###### Compute confidence intervals for estimated parameters (requires Statistics and Machine Learning Toolbox) - MATLAB...
... an NLINResults object or OptimResults object returned by the sbiofit function. ... This MATLAB function computes 95% confidence intervals for the estimated parameters from fitResults, ... Profile Likelihood Confidence Interval Calculation. Define L to be the likelihood, LH, of the parameter estimates (stored in ... The profile likelihood method uses this value as a termination tolerance. For details, see Profile Likelihood Confidence ...